A number of existing techniques can be used to capture the movement of a poly-articulated object. The systems implementing these techniques can be classified into two categories.
The first category combines the systems that use at least one sensor and have recourse to an external device that serves as an absolute reference. By way of illustration, the external device can include one or more cameras situated at known positions. In another example, this external device can include one or more emitters of ultrasound, electromagnetic or other type of energy, said emitters being positioned at known positions in the environment.
The second category combines the systems consisting exclusively of sensors that do not have recourse to an external device serving as an absolute reference. Advantageously, the systems belonging to the second category benefit from an unlimited space of use because the poly-articulated object is not constrained to remain within the operational space of external devices used as absolute reference, this operational space corresponding, for example, to the field of view of a camera or to the range of an emitter. However, the systems of this category exhibit a phenomenon of estimation drift because they are deprived of any reference relative to the environment. In other words, the error in the estimations of the positions relative to the environment of the elements that make up the poly-articulated object is not bounded in time. The elements that make up the poly-articulated object are called segments.
Some systems belonging to the second category, that is to say comprising only sensors that do not have recourse to an external device serving as absolute reference, use inertial/magnetic sensors such as, for example, gyrometers, accelerometers or magnetometers. These sensors can be implemented by using the technology of micro-electro-mechanical systems (MEMS). This makes it possible to obtain compact and lightweight sensors. Thus, it is possible to instrument the poly-articulated object with this type of sensor without hampering its movement. Since the MEMS sensors are low energy consumers, they are often embedded onboard small independent and energy-autonomous modules which transmit their information to a central unit via a wireless link. Moreover, the MEMS sensors are particularly inexpensive.
Hereinafter in the description, the word “accelerometer” denotes a sensor capable of measuring the vector sum of its proper acceleration and of gravity. The word “gyrometer” denotes a sensor capable of measuring the rotational speed vector relative to an immobile reference frame. The word “magnetometer” denotes a sensor capable of measuring the Earth's magnetic field vector. The measurements are expressed in the reference frame of the sensor.
Estimating the translational position of a free mobile object in space via measurements from MEMS inertial/magnetic sensors is a problem that is difficult to resolve. The only information that emerges from the translation is the measurement from the accelerometer. To obtain a translational position, it is possible to integrate proper acceleration twice as a function of time. Since an accelerometer measures both the proper acceleration of the object to which it is attached but also the acceleration linked to the ambient gravitational field, it is necessary to extract gravity from the accelerometer measurement before performing the double time integration. The capacity to cancel the effects of gravity depends on the capacity of the system to finely estimate its orientation, and to do so for all the dynamics of the movement.
Even a very small error of a milli-radian (mrad) in the estimation of the orientation induces an error of 0.01 m/s2 on the extracted proper acceleration. If this error is not corrected, it is translated, after the double integration, into an error of 4.5 meters at the end of thirty seconds. The orientation estimation errors of the devices are generally significantly greater than a milli-radian, all the more so as the efficiency of the methods is very generally dependent on the dynamics of the movement captured. Thus, if this estimation drift is not reduced, an accurate estimation of the translation of a free mobile object is impossible.
A movement estimation system that makes it possible to reduce this drift is described in U.S. Pat. No. 8,165,844. The proposed solution relies on a device made up of a set of capture modules arranged on segments of the poly-articulated object. Each module is equipped with inertial/magnetic sensors (accelerometer, gyrometer and possibly magnetometer). The device further comprises a merging circuit that makes it possible to estimate the position and the orientation of the segments. The merging is based on the inertial/magnetic measurements originating from the capture modules. The measurements can be collected by wire or wirelessly.
The method making it possible to reduce the drift on the estimation of the position comprises an estimation phase and a correction phase.
The estimation phase comprises two steps. In a first step, kinematic quantities are estimated for each capture module from the measurements that they supply. The expression “kinematic quantities” denotes the position, the speed and the acceleration of a rigid body. These quantities are expressed relative to the capture module and not relative to the segment to which the sensor is attached. In a second step, the kinematic quantities of the segments are estimated based on the knowledge of the positioning of the sensors on the segments and the morphology of the poly-articulated object by using, for example, the length of the segments. The output from the estimation phase is an estimation of the kinematic quantities of the segments.
The correction phase improves this first estimation. It can contain up to three steps. In a first step, the limitations linked to the articulations are used to correct the estimation of the kinematic quantities of the segments. By way of example, an information item indicating a prohibited rotation about a particular axis can be used. In a second step, the detection of external contacts is used to correct the estimation of the kinematic quantities of the segments. This detection is made by using an external sensor or a heuristic based on the kinematic quantities of the segments expressed at particular points. In a third step, the measurements of external sensors are used in order to correct the estimation of the kinematic quantities of the segments. The external sensors are, for example, of GPS (global positioning system) or RFID (radio frequency identification) type. The data presented as output from the three steps of the correction phase are identical. They are an estimation of the kinematic quantities of the segments. The correction phase of this method therefore corresponds to a series of successive individual corrections.
A translational drift inherent in this method is the consequence of the use of a double time integration of the estimates of the proper acceleration for each sensor. If these estimates are very slightly incorrect, the estimates of the translational positions of the sensors are very different from their real values. The correction phase is implemented to correct these errors.
The various embodiments of the invention described hereinbelow offer an alternative to this method that makes it possible to reduce the drift on the estimation of the translational position by avoiding having recourse to a double time integration of the estimate of the proper acceleration at the level of each sensor.